Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada.
Department of Medicine, University of Toronto, Toronto, ON, Canada.
J Gen Intern Med. 2023 Nov;38(15):3303-3312. doi: 10.1007/s11606-023-08245-w. Epub 2023 Jun 9.
Methods to accurately predict the risk of in-hospital mortality are important for applications including quality assessment of healthcare institutions and research.
To update and validate the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin which is difficult to standardize across modern clinical assays.
Retrospective cohort study using electronic health record data from GEMINI. GEMINI is a research collaborative that collects administrative and clinical data from hospital information systems.
Adult general medicine inpatients at 28 hospitals in Ontario, Canada, between April 2010 and December 2022.
The outcome was in-hospital mortality, modeled by diagnosis group using 56 logistic regressions. We compared models with and without troponin as an input to the laboratory-based acute physiology score. We fit and validated the updated method using internal-external cross-validation at 28 hospitals from April 2015 to December 2022.
In 938,103 hospitalizations with 7.2% in-hospital mortality, the updated KP method accurately predicted the risk of mortality. The c-statistic at the median hospital was 0.866 (see Fig. 3) (25th-75th 0.848-0.876, range 0.816-0.927) and calibration was strong for nearly all patients at all hospitals. The 95th percentile absolute difference between predicted and observed probabilities was 0.038 at the median hospital (25th-75th 0.024-0.057, range 0.006-0.118). Model performance was very similar with and without troponin in a subset of 7 hospitals, and performance was similar with and without troponin for patients hospitalized for heart failure and acute myocardial infarction.
An update to the KP method accurately predicted in-hospital mortality for general medicine inpatients in 28 hospitals in Ontario, Canada. This updated method can be implemented in a wider range of settings using common open-source tools.
准确预测住院患者死亡风险的方法对于医疗质量评估和研究等应用非常重要。
使用开源工具测量合并症和诊断组，并去除难以在现代临床检测中标准化的肌钙蛋白，更新和验证 Kaiser Permanente 住院患者风险调整方法（KP 方法）以预测住院死亡率。
使用 GEMINI 的电子健康记录数据进行回顾性队列研究。GEMINI 是一个研究协作组织，从医院信息系统中收集管理和临床数据。
2010 年 4 月至 2022 年 12 月期间，加拿大安大略省 28 家医院的成年综合医学住院患者。
结局为住院死亡率，使用 56 个逻辑回归模型通过诊断组进行建模。我们比较了有无肌钙蛋白作为实验室急性生理学评分输入的模型。我们使用 2015 年 4 月至 2022 年 12 月期间 28 家医院的内部-外部交叉验证来拟合和验证更新后的方法。
在 938103 例住院患者中，死亡率为 7.2%，更新后的 KP 方法准确预测了死亡率风险。中位数医院的 c 统计量为 0.866（见图 3）（25 至 75 分位值为 0.848-0.876，范围为 0.816-0.927），几乎所有患者在所有医院的校准都很强。中位数医院预测和观察到的概率之间的 95%百分位绝对差值为 0.038（25 至 75 分位值为 0.024-0.057，范围为 0.006-0.118）。在 7 家医院的一个子集中，有无肌钙蛋白的模型性能非常相似，对于心力衰竭和急性心肌梗死住院患者，有无肌钙蛋白的模型性能也相似。
对 KP 方法的更新准确预测了加拿大安大略省 28 家医院综合医学住院患者的住院死亡率。这种更新后的方法可以使用常见的开源工具在更广泛的环境中实施。
